1. Technical Field
The present invention relates to image analysis, and more particularly to a candidate generation method for generating a list of targeted candidates from 3D volumetric data.
2. Discussion of Related Art
A candidate generation method that is able to reliably and accurately detect nodule candidates from input 3D volumetric data plays a critical role in automatic nodule detection. In a typical 3D volumetric data (with a dimension of 512 by 512 by 300), non-nodule (background tissue) structures such as vessel trees, which includes of the dominating portion of the distinguishable objects in the volumetric data, are extreme complex in formation. Targeted nodules, on the other hand, merely are a few compact round shaped objects, which reside nearby or occlude with the complex background tissue structures. There is no discriminating feature that can be easily determined to differentiate the targeted nodules from the complex background tissue structures. There are a huge number of locations where background tissues exhibit nodule-like properties. It is very difficult to design a method that is able to reliably and accurately identify the few true positions where true nodule present by efficiently reject those huge number of impostor locations. In addition, the amount of information needs to be processed in 3D volumetric data is huge (a chest HRCT (high resolution computer tomography) data is typically of dimension 512×512×300). It is typically not practical to employ a technique applying sophisticated and computationally expensive analysis to every position (voxel) in 3D volumetric data.
Therefore, a need exists for a system and method for a computationally efficient candidate generation method.